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danielyeh2026/
xvla-libero10-repro

Name: xvla-libero10-repro
Availability: InStock

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Model Card for xvla

X-VLA is a soft-prompted, flow-matching Vision-Language-Action framework that treats each robot or hardware setup as a "task" encoded with a small set of learnable Soft Prompt embeddings, letting a single model reconcile diverse robot morphologies, sensors, and action spaces.

This policy has been trained and pushed to the Hub using LeRobot.

Learn how to train and run it in the LeRobot xvla guide, or browse the full documentation.

Model Details

License: apache-2.0
Fine-tuned from: lerobot/xvla-base
Robot type: panda
Cameras: image, wrist_image

Inputs & Outputs

The policy consumes these observation features and produces these action features.

Inputs

Feature	Type	Shape
`observation.images.image`	VISUAL	`(3, 256, 256)`
`observation.images.image2`	VISUAL	`(3, 256, 256)`
`observation.state`	STATE	`(8,)`
`observation.images.image3`	VISUAL	`(3, 224, 224)`
`observation.images.empty_camera_0`	VISUAL	`(3, 224, 224)`

Outputs

Feature	Type	Shape
`action`	ACTION	`(7,)`

Training Dataset

Repository: lerobot/libero_10
Episodes: 379
Frames: 101469
Frame rate: 10 FPS
Task(s): "put the white mug on the left plate and put the yellow and white mug on the right plate", "put the white mug on the plate and put the chocolate pudding to the right of the plate", "put the yellow and white mug in the microwave and close it", "turn on the stove and put the moka pot on it", "put both the alphabet soup and the cream cheese box in the basket", "put both the alphabet soup and the tomato sauce in the basket", "put both moka pots on the stove", "put both the cream cheese box and the butter in the basket", "put the black bowl in the bottom drawer of the cabinet and close it", "pick up the book and place it in the back compartment of the caddy"

Training Configuration

Setting	Value
Training steps	10
Batch size	32
Optimizer	xvla-adamw
Learning rate	0.0001
Seed	1000
LeRobot version	0.5.2

How to Get Started with the Model

New to LeRobot? These guides cover the full workflow:

Install LeRobot — set up the lerobot package.
Hardware setup — assemble, wire, and calibrate your robot and cameras.
Record data & train a policy — the end-to-end imitation-learning walkthrough.
CLI cheat-sheet — quick reference for the lerobot-* commands.

The short version to run and train this policy:

Run the policy on your robot

bash

lerobot-rollout \
  --strategy.type=base \
  --robot.type=panda \
  --robot.port=<your_robot_port> \
  --robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
  --policy.path=danielyeh2026/xvla-libero10-repro \
  --task="put the white mug on the left plate and put the yellow and white mug on the right plate" \
  --duration=60

Replace the remaining <...> placeholders with your own values: --robot.port and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on.

When --strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at rollout documentation.

Train your own policy

This policy type is usually fine-tuned from the pretrained base model lerobot/xvla-base:

bash

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.path=lerobot/xvla-base \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true

Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.

Evaluation

No evaluation results have been provided for this policy yet.

Citation

If you use this policy, please cite the method linked in the description above, along with LeRobot:

bibtex

@misc{cadene2024lerobot,
    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
    howpublished = "\url{https://github.com/huggingface/lerobot}",
    year = {2024}
}